The Graph priceGRT

UNDERSTANDING THE CONTenders The token Allora (ALLO) currently has a market capitalisation of about US$70 million. To see how this stacks up in the broader space, we should compare Allora with other cryptocurrencies that focus on artificial intelligence or decentralised AI‑related networks. Some of the larger peers include: • Bittensor (TAO ) which has a market cap in the billions. • Render (RNDR) a network for decentralised GPU/compute power, also in the multi‑billion dollar range. • The Graph (GRT) which is less intensely AI‑centric, but still part of the infrastructure layer and has a market cap well over a billion. WHERE ALLORA SITS At ~US$70 million, Allora sits on the much smaller end of this spectrum. This can be interpreted in two ways: • On the positive side: Allora may have more room to grow relative to the larger players if it delivers on its vision. • On the caution side: It also suggests that Allora has fewer resources, possibly less market confidence, fewer adopters, and more risk compared to the major projects. BENEFITS OF COMPARISON By comparing market caps we gain insights such as: • Relative maturity – Bigger market caps often mean more adoption, larger networks, strong partnerships, more liquidity. Allora being smaller could mean it is earlier stage. • Risk vs reward – Smaller market cap means higher potential upside (if the project succeeds) but also higher downside (if it fails or stalls). • Benchmarking growth – If Allora can demonstrate similar growth trajectory to Bittensor or Render, then moving from tens of millions to hundreds of millions or billions is theoretically possible—but it is a long shot. • Valuation context – Understanding that many AI‑crypto projects are already valued in billions helps one appreciate how Allora’s current market cap fits in the narrative of “emerging” rather than “established”. KEY TAKEAWAYS FOR ALLORA • Allora is significantly smaller in market cap compared to its major AI‑crypto peers. That means potential for growth but also greater risk of stagnation. • If Allora can progress its utility, grow its ecosystem, and gain adoption, it might move toward a higher valuation band. • However, many projects with large valuations have already built large networks hence the jump for Allora will be challenging and will require strong execution. • For someone learning or exploring, it means that Allora is early. Early can be exciting but one should keep expectations realistic and consider both upside and similarity of risk. In simple terms: Allora’s market cap (~US$70 million) is small when compared with leading AI‑blockchain tokens which have market caps in the billions. That difference signals that Allora is in the early phase. This gives it room to grow—but also means more uncertainty. When comparing it to its peers, one can see both the potential and the challenge. Understanding this helps anyone considering Allora to place it properly in the landscape of AI‑crypto tokens. $ALLO $TAO $GRT

1. Overview of TRUST Purpose & positioning Intuition positions itself as a decentralized “knowledge graph” network: it turns information (Atoms & Triples) into on‑chain, token‑curated assets. TRUST is the native token (max supply ~ 1 billion) used for staking, curation, governance, network fees (creating data, paying queries) in the Intuition ecosystem. Network launched mainnet around late 2025 with pre‑testnet traction (~244k participants) and partnership/support from players like ConsenSys and others. Key token‐economics / utility highlights Data creation and curation: Users pay TRUST fees to publish data into the graph; staking of TRUST behind claims gives reputation/weight. Governance and staking: Users lock TRUST (veTRUST model) to gain voting rights and reward weight. Access & query fees: Consumers pay TRUST to query indexed/verifiable knowledge. Strengths / things to monitor Novel “Info‑Fi” / knowledge‑graph niche: turning information into tokenised assets is a differentiator. Token utility appears tightly tied to network usage (if adoption happens). Early stage gives potential upside if the use‐case scales. ‑ Risks: Adoption challenge (network effect, killer apps), competitive landscape, token supply/distribution and demand ramp. ‑ Being early means potentially higher execution risk. 2. Peer Tokens: Selected Comparators I pick two relevant comparators in the data / knowledge / indexing infrastructure space: A) GRT (the token of The Graph) The Graph is a decentralized indexing protocol that allows apps to query blockchain data via Subgraphs. GRT is used for staking (indexers, curators, delegators), paying for queries, governance. Tokenomics: Max supply ~ 10 billion GRT; network inflation ~3%/year; token supply and circulating amount are significant. Strengths & risks Proven real‐world usage (many dApps rely on The Graph). Clearly defined ecosystem and token role. ‑ Strong supply, inflation, market expectations may be baked in. ‑ Less “early stage” than TRUST; upside may be more measured. B) OCEAN (the token of Ocean Protocol) Ocean Protocol is a decentralized data marketplace: data NFT + datatoken architecture, “compute‑to‑data”, monetisation of data assets. OCEAN token is used for staking on datasets, governance, and marketplace exchange. Strengths & risks Focused on unlocking data for AI and analytic use‐cases — high relevance. Established protocol compared to pure greenfield. ‑ Data markets are competitive; execution and adoption remain key. ‑ Token may face similar supply/demand and speculative risks. 3. Comparative Table: TRUST vs GRT vs OCEAN FeatureTRUSTGRTOCEANPrimary Use CaseToken‐curated knowledge graph (Atoms/Triples) – InfoFi / data/knowledge layerDecentralised indexing of blockchain data (subgraphs)Data marketplace & monetisation — data/AI layerToken UtilityStaking for claims; fees for data creation/query; governance.Indexer/delegator/curator roles; query payments; governance.Staking on data assets; governance; token for data marketplace.Stage of AdoptionEarly stage (mainnet just launched)More mature; broadly used by dAppsIntermediate maturity; used in data marketplacesToken Supply / Economics~1 billion max supply; utility tightly defined. ~10 billion max; inflation ~3%/yr. Supply & distribution vary; focus on data‐token model; ecosystem reliant on marketplace activity. Upside PotentialHigher if InfoFi / knowledge economy takes off and network effect buildsModerate—already somewhat priced in; more establishedModerate to high if data/AI demand surges and protocol executesKey RisksExecution risk; adoption; ecosystem build‐out; token demand rampSupply inflation; slower upside; comparable competitionCompetition; need for high data quality/market usage; token demand tied to marketplace liquidityDifferentiating EdgeNiche of a trust layer for knowledge graph + AI‑oriented visionStrong infrastructure; developer adoption; indexing stackData monetisation + AI/data economy tie‑in; unique marketplace model 4. What this means for market analysis on Bitget When you analyse TRUST on Bitget (or in general), you can draw the following conclusions from the comparison: Demand driver is key: For TRUST to succeed (in token value) you’ll want to see increasing usage of the Intuition network — i.e., more claims, queries, staking, ecosystem build‑out. Without usage, the token utility is weak. Competition & positioning matter: TRUST is entering a space where GRT and OCEAN already exist. Its differentiator is the “knowledge graph / trust layer” angle; you should assess whether that gives it enough market niche and moat. Token economics and supply matter: With TRUST’s max supply lower, there may be supply advantage compared to some peers. But demand must ramp. For GRT and OCEAN you’ll need to factor inflation, circulating supply unlocks, and existing market saturation. Adoption timeline & events: Since TRUST is early stage, listings (such as on Bitget), partnerships, mainnet usage milestones will have larger impact (both upside and risk) compared to more mature tokens. Volatility & risk premium: Early stage = higher risk/higher reward. Investors/traders should expect more volatility; regulators, token unlock schedules, liquidity, and sentiment swings will matter. Valuation benchmark: Comparing metrics (such as network usage, token velocity, staking participation, circulating supply) with peer tokens can help set relative valuation or upside potential. For example: if GRT commands X valuation given its usage, what would TRUST need in terms of usage to justify similar or higher valuation? 5. Suggested Angles for Your Article Here are some strong article headings and angles you could take: “Can TRUST Out‑Index The Graph? – Benchmarking usage, economics & upside” Focus: compare TRUST vs GRT directly: usage metrics, tokenomics, adoption path. “Knowledge as Asset: How TRUST vs OCEAN approach the data economy” Focus: compare TRUST’s “knowledge graph” model vs OCEAN’s “data marketplace” model; highlight different verticals. “Tokenomics deep dive: TRUST vs infrastructure peers — demand, supply & valuation implications” Focus on supply, staking, incentives, token unlock schedules, and how that affects price potential. “Execution roadmap matters: What TRUST must deliver to compete with GRT and OCEAN” Focus: milestones, network adoption, partnerships, liquidity, listing effects (e.g., on Bitget), vs peers which have head‑start. 6. Key Metrics / Data to Gather To turn the analysis into a full article, you’ll want to pull in the following metrics: Number of active users / stakers / curators on Intuition network (TRUST) → growth trend. Total value staked (TVS) of TRUST token. Query volume / data usage on the network. Token distribution / unlock schedule for TRUST (who holds, how many locked/unlocked). Circulating supply vs max supply for TRUST and peer tokens. Usage metrics of peers (GRT, OCEAN) such as number of queries served (for GRT), datasets published/bought (for OCEAN) to benchmark. Market cap and current price metrics of all three (for relative valuation). Listing details and exchange liquidity (e.g., on Bitget) for TRUST. Roadmap milestones and upcoming catalysts (for TRUST) and current status (for peers). 7. Conclusion & Takeaway In short: TRUST is an ambitious entrant into an important niche — knowledge‐graph / trust layer for information/AI. Compared with peers like GRT and OCEAN, it offers potential upside because of its early stage and focused use‐case. However, the upside is conditional on execution, adoption, network utility and token demand. For a Bitget analysis article: emphasise what must go right for TRUST, how it stacks up versus existing players, and what risk/return profile it offers relative to its peers.

Doesn't this smell like #altseason? In the past 24 hours, we saw a very strong rebound with the total crypto market cap climbing back over $3.46 trillion. $Bitcoin dominance is now at 58%, while alts collectively added over $30 billion in market cap. Pay attention to #AI coins: $FET up +46.0% $RNDR up +14.5% $NEAR up +28.8% $TAO up+4.3% $GRT up +13–15% I think #AI and #DEFAI will get another run soon, so will be smart to start positioning. Overall, market bias is pivoting bullish for alts, with liquidity flowing into undervalued narratives. Still volatile. Limit your trades and turn on scalp mode.

Galaxy Research reports that 72 of the top 100 cryptocurrencies remain over 50% below their all-time highs. FIL, ICP, and GRT show the steepest declines among major tokens, while others like DOT, AVAX, and ADA also fell sharply. Only a few assets including BTC, LEO, ETH, and BNB posted smaller losses.